Overview

Dataset statistics

Number of variables22
Number of observations32950
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 MiB
Average record size in memory176.0 B

Variable types

Numeric11
Categorical11

Alerts

id is highly correlated with emp.var.rate and 3 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
emp.var.rate is highly correlated with id and 3 other fieldsHigh correlation
cons.price.idx is highly correlated with id and 1 other fieldsHigh correlation
euribor3m is highly correlated with id and 2 other fieldsHigh correlation
nr.employed is highly correlated with id and 2 other fieldsHigh correlation
id is highly correlated with emp.var.rate and 3 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdays and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with id and 3 other fieldsHigh correlation
cons.price.idx is highly correlated with id and 3 other fieldsHigh correlation
euribor3m is highly correlated with id and 3 other fieldsHigh correlation
nr.employed is highly correlated with id and 4 other fieldsHigh correlation
id is highly correlated with cons.price.idxHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with id and 1 other fieldsHigh correlation
euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
nr.employed is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
contact is highly correlated with monthHigh correlation
month is highly correlated with contactHigh correlation
housing is highly correlated with loanHigh correlation
loan is highly correlated with housingHigh correlation
id is highly correlated with contact and 9 other fieldsHigh correlation
age is highly correlated with jobHigh correlation
job is highly correlated with age and 1 other fieldsHigh correlation
education is highly correlated with jobHigh correlation
housing is highly correlated with loanHigh correlation
loan is highly correlated with housingHigh correlation
contact is highly correlated with id and 6 other fieldsHigh correlation
month is highly correlated with id and 6 other fieldsHigh correlation
pdays is highly correlated with id and 5 other fieldsHigh correlation
previous is highly correlated with pdays and 2 other fieldsHigh correlation
poutcome is highly correlated with id and 7 other fieldsHigh correlation
emp.var.rate is highly correlated with id and 7 other fieldsHigh correlation
cons.price.idx is highly correlated with id and 7 other fieldsHigh correlation
cons.conf.idx is highly correlated with id and 9 other fieldsHigh correlation
euribor3m is highly correlated with id and 10 other fieldsHigh correlation
nr.employed is highly correlated with id and 9 other fieldsHigh correlation
y is highly correlated with id and 3 other fieldsHigh correlation
id is uniformly distributed Uniform
id has unique values Unique
previous has 28437 (86.3%) zeros Zeros

Reproduction

Analysis started2022-06-01 10:57:55.296309
Analysis finished2022-06-01 10:59:12.266920
Duration1 minute and 16.97 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct32950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20612.91942
Minimum0
Maximum41187
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size257.5 KiB
2022-06-01T16:29:12.523305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2094.9
Q110373.25
median20610.5
Q330858.75
95-th percentile39145.55
Maximum41187
Range41187
Interquartile range (IQR)20485.5

Descriptive statistics

Standard deviation11872.66073
Coefficient of variation (CV)0.5759815231
Kurtosis-1.196037703
Mean20612.91942
Median Absolute Deviation (MAD)10243.5
Skewness0.0009647821938
Sum679195695
Variance140960072.7
MonotonicityNot monotonic
2022-06-01T16:29:12.869851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125561
 
< 0.1%
167051
 
< 0.1%
329991
 
< 0.1%
193391
 
< 0.1%
154891
 
< 0.1%
10181
 
< 0.1%
175631
 
< 0.1%
51931
 
< 0.1%
157011
 
< 0.1%
35361
 
< 0.1%
Other values (32940)32940
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
21
< 0.1%
31
< 0.1%
51
< 0.1%
61
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
121
< 0.1%
ValueCountFrequency (%)
411871
< 0.1%
411861
< 0.1%
411851
< 0.1%
411841
< 0.1%
411831
< 0.1%
411821
< 0.1%
411811
< 0.1%
411801
< 0.1%
411791
< 0.1%
411781
< 0.1%

age
Real number (ℝ≥0)

HIGH CORRELATION

Distinct77
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.01742033
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.5 KiB
2022-06-01T16:29:13.350897image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.43584186
Coefficient of variation (CV)0.2607824736
Kurtosis0.7872153755
Mean40.01742033
Median Absolute Deviation (MAD)7
Skewness0.7861920227
Sum1318574
Variance108.9067954
MonotonicityNot monotonic
2022-06-01T16:29:13.689270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311551
 
4.7%
321508
 
4.6%
331476
 
4.5%
361443
 
4.4%
301391
 
4.2%
351383
 
4.2%
341380
 
4.2%
291167
 
3.5%
371165
 
3.5%
381145
 
3.5%
Other values (67)19341
58.7%
ValueCountFrequency (%)
173
 
< 0.1%
1824
 
0.1%
1934
 
0.1%
2055
 
0.2%
2177
 
0.2%
22110
 
0.3%
23186
 
0.6%
24369
1.1%
25480
1.5%
26563
1.7%
ValueCountFrequency (%)
982
 
< 0.1%
951
 
< 0.1%
924
 
< 0.1%
912
 
< 0.1%
892
 
< 0.1%
8816
< 0.1%
871
 
< 0.1%
863
 
< 0.1%
8512
< 0.1%
847
< 0.1%

job
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
admin.
8328 
blue-collar
7439 
technician
5352 
services
3212 
management
2310 
Other values (7)
6309 

Length

Max length13
Median length12
Mean length8.954385432
Min length6

Characters and Unicode

Total characters295047
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblue-collar
2nd rowadmin.
3rd rowretired
4th rowhousemaid
5th rowadmin.

Common Values

ValueCountFrequency (%)
admin.8328
25.3%
blue-collar7439
22.6%
technician5352
16.2%
services3212
 
9.7%
management2310
 
7.0%
retired1363
 
4.1%
self-employed1153
 
3.5%
entrepreneur1145
 
3.5%
housemaid867
 
2.6%
unemployed798
 
2.4%
Other values (2)983
 
3.0%

Length

2022-06-01T16:29:14.113313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin8328
25.3%
blue-collar7439
22.6%
technician5352
16.2%
services3212
 
9.7%
management2310
 
7.0%
retired1363
 
4.1%
self-employed1153
 
3.5%
entrepreneur1145
 
3.5%
housemaid867
 
2.6%
unemployed798
 
2.4%
Other values (2)983
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e37784
12.8%
n28247
 
9.6%
a26606
 
9.0%
l25421
 
8.6%
i24474
 
8.3%
c21355
 
7.2%
r16812
 
5.7%
m15766
 
5.3%
d13230
 
4.5%
t11612
 
3.9%
Other values (14)73740
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter278127
94.3%
Dash Punctuation8592
 
2.9%
Other Punctuation8328
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e37784
13.6%
n28247
10.2%
a26606
9.6%
l25421
9.1%
i24474
8.8%
c21355
 
7.7%
r16812
 
6.0%
m15766
 
5.7%
d13230
 
4.8%
t11612
 
4.2%
Other values (12)56820
20.4%
Dash Punctuation
ValueCountFrequency (%)
-8592
100.0%
Other Punctuation
ValueCountFrequency (%)
.8328
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin278127
94.3%
Common16920
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e37784
13.6%
n28247
10.2%
a26606
9.6%
l25421
9.1%
i24474
8.8%
c21355
 
7.7%
r16812
 
6.0%
m15766
 
5.7%
d13230
 
4.8%
t11612
 
4.2%
Other values (12)56820
20.4%
Common
ValueCountFrequency (%)
-8592
50.8%
.8328
49.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII295047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e37784
12.8%
n28247
 
9.6%
a26606
 
9.0%
l25421
 
8.6%
i24474
 
8.3%
c21355
 
7.2%
r16812
 
5.7%
m15766
 
5.3%
d13230
 
4.5%
t11612
 
3.9%
Other values (14)73740
25.0%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
married
19823 
single
9333 
divorced
3733 
unknown
 
61

Length

Max length8
Median length7
Mean length6.830045524
Min length6

Characters and Unicode

Total characters225050
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowdivorced
5th rowsingle

Common Values

ValueCountFrequency (%)
married19823
60.2%
single9333
28.3%
divorced3733
 
11.3%
unknown61
 
0.2%

Length

2022-06-01T16:29:14.604080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:14.970624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
married19823
60.2%
single9333
28.3%
divorced3733
 
11.3%
unknown61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r43379
19.3%
i32889
14.6%
e32889
14.6%
d27289
12.1%
m19823
8.8%
a19823
8.8%
n9516
 
4.2%
s9333
 
4.1%
g9333
 
4.1%
l9333
 
4.1%
Other values (6)11443
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter225050
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r43379
19.3%
i32889
14.6%
e32889
14.6%
d27289
12.1%
m19823
8.8%
a19823
8.8%
n9516
 
4.2%
s9333
 
4.1%
g9333
 
4.1%
l9333
 
4.1%
Other values (6)11443
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin225050
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r43379
19.3%
i32889
14.6%
e32889
14.6%
d27289
12.1%
m19823
8.8%
a19823
8.8%
n9516
 
4.2%
s9333
 
4.1%
g9333
 
4.1%
l9333
 
4.1%
Other values (6)11443
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII225050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r43379
19.3%
i32889
14.6%
e32889
14.6%
d27289
12.1%
m19823
8.8%
a19823
8.8%
n9516
 
4.2%
s9333
 
4.1%
g9333
 
4.1%
l9333
 
4.1%
Other values (6)11443
 
5.1%

education
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
university.degree
9750 
high.school
7614 
basic.9y
4808 
professional.course
4176 
basic.4y
3338 
Other values (3)
3264 

Length

Max length19
Median length17
Mean length12.70880121
Min length7

Characters and Unicode

Total characters418755
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.9y
2nd rowuniversity.degree
3rd rowbasic.4y
4th rowbasic.9y
5th rowhigh.school

Common Values

ValueCountFrequency (%)
university.degree9750
29.6%
high.school7614
23.1%
basic.9y4808
14.6%
professional.course4176
12.7%
basic.4y3338
 
10.1%
basic.6y1852
 
5.6%
unknown1399
 
4.2%
illiterate13
 
< 0.1%

Length

2022-06-01T16:29:15.326305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:15.764411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
university.degree9750
29.6%
high.school7614
23.1%
basic.9y4808
14.6%
professional.course4176
12.7%
basic.4y3338
 
10.1%
basic.6y1852
 
5.6%
unknown1399
 
4.2%
illiterate13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e47378
 
11.3%
i41314
 
9.9%
s39890
 
9.5%
.31538
 
7.5%
o29155
 
7.0%
r27865
 
6.7%
h22842
 
5.5%
c21788
 
5.2%
y19748
 
4.7%
n18123
 
4.3%
Other values (15)119114
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter377219
90.1%
Other Punctuation31538
 
7.5%
Decimal Number9998
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e47378
12.6%
i41314
11.0%
s39890
10.6%
o29155
 
7.7%
r27865
 
7.4%
h22842
 
6.1%
c21788
 
5.8%
y19748
 
5.2%
n18123
 
4.8%
g17364
 
4.6%
Other values (11)91752
24.3%
Decimal Number
ValueCountFrequency (%)
94808
48.1%
43338
33.4%
61852
 
18.5%
Other Punctuation
ValueCountFrequency (%)
.31538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin377219
90.1%
Common41536
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e47378
12.6%
i41314
11.0%
s39890
10.6%
o29155
 
7.7%
r27865
 
7.4%
h22842
 
6.1%
c21788
 
5.8%
y19748
 
5.2%
n18123
 
4.8%
g17364
 
4.6%
Other values (11)91752
24.3%
Common
ValueCountFrequency (%)
.31538
75.9%
94808
 
11.6%
43338
 
8.0%
61852
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII418755
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e47378
 
11.3%
i41314
 
9.9%
s39890
 
9.5%
.31538
 
7.5%
o29155
 
7.0%
r27865
 
6.7%
h22842
 
5.5%
c21788
 
5.2%
y19748
 
4.7%
n18123
 
4.3%
Other values (15)119114
28.4%

default
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
no
26090 
unknown
6857 
yes
 
3

Length

Max length7
Median length2
Mean length3.04060698
Min length2

Characters and Unicode

Total characters100188
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowno
3rd rowno
4th rowno
5th rowunknown

Common Values

ValueCountFrequency (%)
no26090
79.2%
unknown6857
 
20.8%
yes3
 
< 0.1%

Length

2022-06-01T16:29:16.110230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:16.396752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no26090
79.2%
unknown6857
 
20.8%
yes3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n46661
46.6%
o32947
32.9%
u6857
 
6.8%
k6857
 
6.8%
w6857
 
6.8%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter100188
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n46661
46.6%
o32947
32.9%
u6857
 
6.8%
k6857
 
6.8%
w6857
 
6.8%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin100188
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n46661
46.6%
o32947
32.9%
u6857
 
6.8%
k6857
 
6.8%
w6857
 
6.8%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII100188
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n46661
46.6%
o32947
32.9%
u6857
 
6.8%
k6857
 
6.8%
w6857
 
6.8%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

housing
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
yes
17257 
no
14882 
unknown
 
811

Length

Max length7
Median length3
Mean length2.646798179
Min length2

Characters and Unicode

Total characters87212
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowno
3rd rowno
4th rowyes
5th rowno

Common Values

ValueCountFrequency (%)
yes17257
52.4%
no14882
45.2%
unknown811
 
2.5%

Length

2022-06-01T16:29:16.655721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:17.145500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
yes17257
52.4%
no14882
45.2%
unknown811
 
2.5%

Most occurring characters

ValueCountFrequency (%)
n17315
19.9%
y17257
19.8%
e17257
19.8%
s17257
19.8%
o15693
18.0%
u811
 
0.9%
k811
 
0.9%
w811
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter87212
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n17315
19.9%
y17257
19.8%
e17257
19.8%
s17257
19.8%
o15693
18.0%
u811
 
0.9%
k811
 
0.9%
w811
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin87212
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n17315
19.9%
y17257
19.8%
e17257
19.8%
s17257
19.8%
o15693
18.0%
u811
 
0.9%
k811
 
0.9%
w811
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII87212
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n17315
19.9%
y17257
19.8%
e17257
19.8%
s17257
19.8%
o15693
18.0%
u811
 
0.9%
k811
 
0.9%
w811
 
0.9%

loan
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
no
27135 
yes
5004 
unknown
 
811

Length

Max length7
Median length2
Mean length2.274931715
Min length2

Characters and Unicode

Total characters74959
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no27135
82.4%
yes5004
 
15.2%
unknown811
 
2.5%

Length

2022-06-01T16:29:17.424336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:17.742213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
no27135
82.4%
yes5004
 
15.2%
unknown811
 
2.5%

Most occurring characters

ValueCountFrequency (%)
n29568
39.4%
o27946
37.3%
y5004
 
6.7%
e5004
 
6.7%
s5004
 
6.7%
u811
 
1.1%
k811
 
1.1%
w811
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter74959
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n29568
39.4%
o27946
37.3%
y5004
 
6.7%
e5004
 
6.7%
s5004
 
6.7%
u811
 
1.1%
k811
 
1.1%
w811
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin74959
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n29568
39.4%
o27946
37.3%
y5004
 
6.7%
e5004
 
6.7%
s5004
 
6.7%
u811
 
1.1%
k811
 
1.1%
w811
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII74959
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n29568
39.4%
o27946
37.3%
y5004
 
6.7%
e5004
 
6.7%
s5004
 
6.7%
u811
 
1.1%
k811
 
1.1%
w811
 
1.1%

contact
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
cellular
20931 
telephone
12019 

Length

Max length9
Median length8
Mean length8.364764795
Min length8

Characters and Unicode

Total characters275619
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowcellular
3rd rowcellular
4th rowcellular
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular20931
63.5%
telephone12019
36.5%

Length

2022-06-01T16:29:18.148540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:18.468613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
cellular20931
63.5%
telephone12019
36.5%

Most occurring characters

ValueCountFrequency (%)
l74812
27.1%
e56988
20.7%
c20931
 
7.6%
u20931
 
7.6%
a20931
 
7.6%
r20931
 
7.6%
t12019
 
4.4%
p12019
 
4.4%
h12019
 
4.4%
o12019
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter275619
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l74812
27.1%
e56988
20.7%
c20931
 
7.6%
u20931
 
7.6%
a20931
 
7.6%
r20931
 
7.6%
t12019
 
4.4%
p12019
 
4.4%
h12019
 
4.4%
o12019
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin275619
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l74812
27.1%
e56988
20.7%
c20931
 
7.6%
u20931
 
7.6%
a20931
 
7.6%
r20931
 
7.6%
t12019
 
4.4%
p12019
 
4.4%
h12019
 
4.4%
o12019
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII275619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l74812
27.1%
e56988
20.7%
c20931
 
7.6%
u20931
 
7.6%
a20931
 
7.6%
r20931
 
7.6%
t12019
 
4.4%
p12019
 
4.4%
h12019
 
4.4%
o12019
 
4.4%

month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
may
10935 
jul
5775 
aug
4934 
jun
4286 
nov
3314 
Other values (5)
3706 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters98850
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowjul
2nd rowmay
3rd rowmay
4th rowjul
5th rowmay

Common Values

ValueCountFrequency (%)
may10935
33.2%
jul5775
17.5%
aug4934
15.0%
jun4286
 
13.0%
nov3314
 
10.1%
apr2086
 
6.3%
oct574
 
1.7%
sep458
 
1.4%
mar446
 
1.4%
dec142
 
0.4%

Length

2022-06-01T16:29:18.693290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:18.997435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
may10935
33.2%
jul5775
17.5%
aug4934
15.0%
jun4286
 
13.0%
nov3314
 
10.1%
apr2086
 
6.3%
oct574
 
1.7%
sep458
 
1.4%
mar446
 
1.4%
dec142
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a18401
18.6%
u14995
15.2%
m11381
11.5%
y10935
11.1%
j10061
10.2%
n7600
7.7%
l5775
 
5.8%
g4934
 
5.0%
o3888
 
3.9%
v3314
 
3.4%
Other values (7)7566
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter98850
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a18401
18.6%
u14995
15.2%
m11381
11.5%
y10935
11.1%
j10061
10.2%
n7600
7.7%
l5775
 
5.8%
g4934
 
5.0%
o3888
 
3.9%
v3314
 
3.4%
Other values (7)7566
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin98850
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a18401
18.6%
u14995
15.2%
m11381
11.5%
y10935
11.1%
j10061
10.2%
n7600
7.7%
l5775
 
5.8%
g4934
 
5.0%
o3888
 
3.9%
v3314
 
3.4%
Other values (7)7566
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII98850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a18401
18.6%
u14995
15.2%
m11381
11.5%
y10935
11.1%
j10061
10.2%
n7600
7.7%
l5775
 
5.8%
g4934
 
5.0%
o3888
 
3.9%
v3314
 
3.4%
Other values (7)7566
7.7%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
thu
6872 
mon
6793 
tue
6513 
wed
6503 
fri
6269 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters98850
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowtue
5th rowthu

Common Values

ValueCountFrequency (%)
thu6872
20.9%
mon6793
20.6%
tue6513
19.8%
wed6503
19.7%
fri6269
19.0%

Length

2022-06-01T16:29:19.421258image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:19.681200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
thu6872
20.9%
mon6793
20.6%
tue6513
19.8%
wed6503
19.7%
fri6269
19.0%

Most occurring characters

ValueCountFrequency (%)
t13385
13.5%
u13385
13.5%
e13016
13.2%
h6872
7.0%
m6793
6.9%
o6793
6.9%
n6793
6.9%
w6503
6.6%
d6503
6.6%
f6269
6.3%
Other values (2)12538
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter98850
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t13385
13.5%
u13385
13.5%
e13016
13.2%
h6872
7.0%
m6793
6.9%
o6793
6.9%
n6793
6.9%
w6503
6.6%
d6503
6.6%
f6269
6.3%
Other values (2)12538
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin98850
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t13385
13.5%
u13385
13.5%
e13016
13.2%
h6872
7.0%
m6793
6.9%
o6793
6.9%
n6793
6.9%
w6503
6.6%
d6503
6.6%
f6269
6.3%
Other values (2)12538
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII98850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t13385
13.5%
u13385
13.5%
e13016
13.2%
h6872
7.0%
m6793
6.9%
o6793
6.9%
n6793
6.9%
w6503
6.6%
d6503
6.6%
f6269
6.3%
Other values (2)12538
12.7%

duration
Real number (ℝ≥0)

Distinct1472
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean257.7463126
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size257.5 KiB
2022-06-01T16:29:19.976251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median179
Q3318
95-th percentile750.55
Maximum4918
Range4918
Interquartile range (IQR)216

Descriptive statistics

Standard deviation259.4598632
Coefficient of variation (CV)1.006648206
Kurtosis21.49654852
Mean257.7463126
Median Absolute Deviation (MAD)93
Skewness3.333987217
Sum8492741
Variance67319.42063
MonotonicityNot monotonic
2022-06-01T16:29:20.287484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90146
 
0.4%
104135
 
0.4%
106135
 
0.4%
111133
 
0.4%
73133
 
0.4%
85130
 
0.4%
125130
 
0.4%
139130
 
0.4%
136130
 
0.4%
87130
 
0.4%
Other values (1462)31618
96.0%
ValueCountFrequency (%)
04
 
< 0.1%
13
 
< 0.1%
31
 
< 0.1%
46
 
< 0.1%
524
 
0.1%
628
0.1%
744
0.1%
858
0.2%
966
0.2%
1055
0.2%
ValueCountFrequency (%)
49181
< 0.1%
41991
< 0.1%
37851
< 0.1%
36431
< 0.1%
36311
< 0.1%
33661
< 0.1%
33221
< 0.1%
32841
< 0.1%
32531
< 0.1%
31831
< 0.1%

campaign
Real number (ℝ≥0)

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.572959029
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.5 KiB
2022-06-01T16:29:20.682693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.778138286
Coefficient of variation (CV)1.079744471
Kurtosis37.07163233
Mean2.572959029
Median Absolute Deviation (MAD)1
Skewness4.756059229
Sum84779
Variance7.718052338
MonotonicityNot monotonic
2022-06-01T16:29:20.953807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
114105
42.8%
28419
25.6%
34304
 
13.1%
42139
 
6.5%
51261
 
3.8%
6779
 
2.4%
7494
 
1.5%
8327
 
1.0%
9238
 
0.7%
10179
 
0.5%
Other values (31)705
 
2.1%
ValueCountFrequency (%)
114105
42.8%
28419
25.6%
34304
 
13.1%
42139
 
6.5%
51261
 
3.8%
6779
 
2.4%
7494
 
1.5%
8327
 
1.0%
9238
 
0.7%
10179
 
0.5%
ValueCountFrequency (%)
561
 
< 0.1%
432
 
< 0.1%
411
 
< 0.1%
402
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
355
< 0.1%
343
< 0.1%
333
< 0.1%
323
< 0.1%

pdays
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean962.7453414
Minimum0
Maximum999
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size257.5 KiB
2022-06-01T16:29:21.242666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186.248245
Coefficient of variation (CV)0.1934553583
Kurtosis22.43293637
Mean962.7453414
Median Absolute Deviation (MAD)0
Skewness-4.942788102
Sum31722459
Variance34688.40876
MonotonicityNot monotonic
2022-06-01T16:29:21.570837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
99931747
96.3%
3348
 
1.1%
6333
 
1.0%
493
 
0.3%
1250
 
0.2%
747
 
0.1%
947
 
0.1%
246
 
0.1%
1041
 
0.1%
537
 
0.1%
Other values (16)161
 
0.5%
ValueCountFrequency (%)
012
 
< 0.1%
122
 
0.1%
246
 
0.1%
3348
1.1%
493
 
0.3%
537
 
0.1%
6333
1.0%
747
 
0.1%
815
 
< 0.1%
947
 
0.1%
ValueCountFrequency (%)
99931747
96.3%
271
 
< 0.1%
251
 
< 0.1%
222
 
< 0.1%
211
 
< 0.1%
201
 
< 0.1%
192
 
< 0.1%
185
 
< 0.1%
177
 
< 0.1%
168
 
< 0.1%

previous
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1729893778
Minimum0
Maximum7
Zeros28437
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size257.5 KiB
2022-06-01T16:29:21.950857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4929919675
Coefficient of variation (CV)2.849839532
Kurtosis19.57057198
Mean0.1729893778
Median Absolute Deviation (MAD)0
Skewness3.787483862
Sum5700
Variance0.24304108
MonotonicityNot monotonic
2022-06-01T16:29:22.755664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
028437
86.3%
13666
 
11.1%
2598
 
1.8%
3181
 
0.5%
450
 
0.2%
514
 
< 0.1%
63
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
028437
86.3%
13666
 
11.1%
2598
 
1.8%
3181
 
0.5%
450
 
0.2%
514
 
< 0.1%
63
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
63
 
< 0.1%
514
 
< 0.1%
450
 
0.2%
3181
 
0.5%
2598
 
1.8%
13666
 
11.1%
028437
86.3%

poutcome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
nonexistent
28437 
failure
3423 
success
 
1090

Length

Max length11
Median length11
Mean length10.45213961
Min length7

Characters and Unicode

Total characters344398
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rowfailure
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent28437
86.3%
failure3423
 
10.4%
success1090
 
3.3%

Length

2022-06-01T16:29:23.232572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:23.599210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent28437
86.3%
failure3423
 
10.4%
success1090
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n85311
24.8%
e61387
17.8%
t56874
16.5%
i31860
 
9.3%
s31707
 
9.2%
o28437
 
8.3%
x28437
 
8.3%
u4513
 
1.3%
f3423
 
1.0%
a3423
 
1.0%
Other values (3)9026
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter344398
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n85311
24.8%
e61387
17.8%
t56874
16.5%
i31860
 
9.3%
s31707
 
9.2%
o28437
 
8.3%
x28437
 
8.3%
u4513
 
1.3%
f3423
 
1.0%
a3423
 
1.0%
Other values (3)9026
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin344398
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n85311
24.8%
e61387
17.8%
t56874
16.5%
i31860
 
9.3%
s31707
 
9.2%
o28437
 
8.3%
x28437
 
8.3%
u4513
 
1.3%
f3423
 
1.0%
a3423
 
1.0%
Other values (3)9026
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII344398
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n85311
24.8%
e61387
17.8%
t56874
16.5%
i31860
 
9.3%
s31707
 
9.2%
o28437
 
8.3%
x28437
 
8.3%
u4513
 
1.3%
f3423
 
1.0%
a3423
 
1.0%
Other values (3)9026
 
2.6%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0831047041
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative13766
Negative (%)41.8%
Memory size257.5 KiB
2022-06-01T16:29:23.925385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.57001077
Coefficient of variation (CV)18.8919603
Kurtosis-1.059159339
Mean0.0831047041
Median Absolute Deviation (MAD)0.3
Skewness-0.725041921
Sum2738.3
Variance2.464933819
MonotonicityNot monotonic
2022-06-01T16:29:24.437988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.413033
39.6%
-1.87316
22.2%
1.16151
18.7%
-0.12976
 
9.0%
-2.91322
 
4.0%
-3.4860
 
2.6%
-1.7640
 
1.9%
-1.1510
 
1.5%
-3133
 
0.4%
-0.29
 
< 0.1%
ValueCountFrequency (%)
-3.4860
 
2.6%
-3133
 
0.4%
-2.91322
 
4.0%
-1.87316
22.2%
-1.7640
 
1.9%
-1.1510
 
1.5%
-0.29
 
< 0.1%
-0.12976
 
9.0%
1.16151
18.7%
1.413033
39.6%
ValueCountFrequency (%)
1.413033
39.6%
1.16151
18.7%
-0.12976
 
9.0%
-0.29
 
< 0.1%
-1.1510
 
1.5%
-1.7640
 
1.9%
-1.87316
22.2%
-2.91322
 
4.0%
-3133
 
0.4%
-3.4860
 
2.6%

cons.price.idx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57683533
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.5 KiB
2022-06-01T16:29:24.761264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.5791573329
Coefficient of variation (CV)0.00618911006
Kurtosis-0.8310025209
Mean93.57683533
Median Absolute Deviation (MAD)0.38
Skewness-0.2297089258
Sum3083356.724
Variance0.3354232163
MonotonicityNot monotonic
2022-06-01T16:29:25.021431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9946151
18.7%
93.9185372
16.3%
92.8934616
14.0%
93.4444130
12.5%
94.4653531
10.7%
93.22919
8.9%
93.0751956
 
5.9%
92.201612
 
1.9%
92.963571
 
1.7%
92.431360
 
1.1%
Other values (16)2732
8.3%
ValueCountFrequency (%)
92.201612
 
1.9%
92.379213
 
0.6%
92.431360
 
1.1%
92.469139
 
0.4%
92.649287
 
0.9%
92.713133
 
0.4%
92.7569
 
< 0.1%
92.843233
 
0.7%
92.8934616
14.0%
92.963571
 
1.7%
ValueCountFrequency (%)
94.767108
 
0.3%
94.601157
 
0.5%
94.4653531
10.7%
94.215264
 
0.8%
94.199245
 
0.7%
94.055184
 
0.6%
94.027192
 
0.6%
93.9946151
18.7%
93.9185372
16.3%
93.876168
 
0.5%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.5145736
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative32950
Negative (%)100.0%
Memory size257.5 KiB
2022-06-01T16:29:25.305227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.622682714
Coefficient of variation (CV)-0.1140992562
Kurtosis-0.3389691675
Mean-40.5145736
Median Absolute Deviation (MAD)4.4
Skewness0.3097267046
Sum-1334955.2
Variance21.36919548
MonotonicityNot monotonic
2022-06-01T16:29:25.677328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.46151
18.7%
-42.75372
16.3%
-46.24616
14.0%
-36.14130
12.5%
-41.83531
10.7%
-422919
8.9%
-47.11956
 
5.9%
-31.4612
 
1.9%
-40.8571
 
1.7%
-26.9360
 
1.1%
Other values (16)2732
8.3%
ValueCountFrequency (%)
-50.8108
 
0.3%
-50233
 
0.7%
-49.5157
 
0.5%
-47.11956
 
5.9%
-46.24616
14.0%
-45.99
 
< 0.1%
-42.75372
16.3%
-422919
8.9%
-41.83531
10.7%
-40.8571
 
1.7%
ValueCountFrequency (%)
-26.9360
 
1.1%
-29.8213
 
0.6%
-30.1287
 
0.9%
-31.4612
 
1.9%
-33133
 
0.4%
-33.6139
 
0.4%
-34.6130
 
0.4%
-34.8213
 
0.6%
-36.14130
12.5%
-36.46151
18.7%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct313
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.622697572
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.5 KiB
2022-06-01T16:29:25.958105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.733735033
Coefficient of variation (CV)0.4785757018
Kurtosis-1.403300387
Mean3.622697572
Median Absolute Deviation (MAD)0.108
Skewness-0.7112742443
Sum119367.885
Variance3.005837165
MonotonicityNot monotonic
2022-06-01T16:29:26.332702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8572275
 
6.9%
4.9622126
 
6.5%
4.9631990
 
6.0%
4.9611549
 
4.7%
4.856960
 
2.9%
1.405942
 
2.9%
4.964937
 
2.8%
4.965848
 
2.6%
4.96816
 
2.5%
4.864815
 
2.5%
Other values (303)19692
59.8%
ValueCountFrequency (%)
0.6345
 
< 0.1%
0.63537
0.1%
0.63610
 
< 0.1%
0.6376
 
< 0.1%
0.6385
 
< 0.1%
0.63910
 
< 0.1%
0.645
 
< 0.1%
0.64223
0.1%
0.64319
0.1%
0.64428
0.1%
ValueCountFrequency (%)
5.0457
 
< 0.1%
55
 
< 0.1%
4.97141
 
0.4%
4.968774
 
2.3%
4.967513
 
1.6%
4.966501
 
1.5%
4.965848
 
2.6%
4.964937
2.8%
4.9631990
6.0%
4.9622126
6.5%

nr.employed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.094049
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size257.5 KiB
2022-06-01T16:29:26.618140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.35462463
Coefficient of variation (CV)0.01400296258
Kurtosis0.002020044821
Mean5167.094049
Median Absolute Deviation (MAD)37.1
Skewness-1.047957635
Sum170255748.9
Variance5235.191705
MonotonicityNot monotonic
2022-06-01T16:29:27.042713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.113033
39.6%
5099.16805
20.7%
51916151
18.7%
5195.82976
 
9.0%
5076.21322
 
4.0%
5017.5860
 
2.6%
4991.6640
 
1.9%
5008.7511
 
1.6%
4963.6510
 
1.5%
5023.5133
 
0.4%
ValueCountFrequency (%)
4963.6510
 
1.5%
4991.6640
 
1.9%
5008.7511
 
1.6%
5017.5860
 
2.6%
5023.5133
 
0.4%
5076.21322
 
4.0%
5099.16805
20.7%
5176.39
 
< 0.1%
51916151
18.7%
5195.82976
9.0%
ValueCountFrequency (%)
5228.113033
39.6%
5195.82976
 
9.0%
51916151
18.7%
5176.39
 
< 0.1%
5099.16805
20.7%
5076.21322
 
4.0%
5023.5133
 
0.4%
5017.5860
 
2.6%
5008.7511
 
1.6%
4991.6640
 
1.9%

y
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.5 KiB
0
29245 
1
3705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32950
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
029245
88.8%
13705
 
11.2%

Length

2022-06-01T16:29:27.319670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-01T16:29:27.578750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
029245
88.8%
13705
 
11.2%

Most occurring characters

ValueCountFrequency (%)
029245
88.8%
13705
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number32950
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029245
88.8%
13705
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common32950
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029245
88.8%
13705
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII32950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029245
88.8%
13705
 
11.2%

Interactions

2022-06-01T16:29:05.238650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:15.756944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:21.008918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:26.612274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:32.280052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:37.236578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:41.993793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:47.563323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:52.408350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:56.743457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:00.639153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:05.707163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:16.258917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:21.790683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:26.974745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:32.659662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:37.791263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:42.587700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:48.096139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:52.796806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:57.085181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:00.956484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:06.139668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:16.918930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:22.385683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:27.631608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:33.080740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:38.294569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:43.079468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:48.595697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:53.176989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:57.389852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:01.478919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:06.447396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:17.326360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:23.010374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:28.276438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:33.702806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:38.730968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:43.426483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:49.230083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:53.475160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:57.778796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:01.883165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:06.868289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:17.855358image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:23.418927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:28.727326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:34.044383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:39.051757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:43.973069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:49.524089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:53.996274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:58.049984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:02.244320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:07.187274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:18.275377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:23.921310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:29.098435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:34.437690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:39.676378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:44.437728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:49.893889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:54.396063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:58.399483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:02.564193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:07.476615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:18.785815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:24.596142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:29.631167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:34.879057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:40.291771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:45.231904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:50.485018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:54.715606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:58.738742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:02.965253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:07.785391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:19.376930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:25.103695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:30.021164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:35.166722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:40.566175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:45.578403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:50.787298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:55.172378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:59.191575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:03.369334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:08.303619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:19.769942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:25.652733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:30.651417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:35.602519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:40.904137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:45.996399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:51.090750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:55.574733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:59.521225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:04.073012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:08.735058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:20.242688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:25.944724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:31.226541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:36.274920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:41.289381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:46.584697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:51.625914image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:55.970194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:59.826551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:04.505228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:09.180085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:20.641945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:26.267688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:31.666811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:36.768859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:41.565246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:46.946781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:52.098852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:28:56.286656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:00.245733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-01T16:29:04.900380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-06-01T16:29:27.802899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-01T16:29:28.345300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-01T16:29:28.763257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-01T16:29:29.358750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-01T16:29:29.796237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-01T16:29:10.240427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-01T16:29:11.528674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

idagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
01255640blue-collarmarriedbasic.9yunknownyesnotelephonejulmon9429990nonexistent1.493.918-42.74.9605228.10
13545131admin.marrieduniversity.degreenononocellularmaymon11649990nonexistent-1.892.893-46.21.2445099.10
23059259retiredmarriedbasic.4ynononocellularmaymon1369991failure-1.892.893-46.21.3545099.10
31791443housemaiddivorcedbasic.9ynoyesnocellularjultue9459990nonexistent1.493.918-42.74.9615228.10
4331539admin.singlehigh.schoolunknownnonotelephonemaythu34429990nonexistent1.193.994-36.44.8605191.00
52919133managementmarriedbasic.9ynononocellularaprfri72729990nonexistent-1.893.075-47.11.4055099.10
63054935admin.marriedprofessional.coursenoyesnocellularmaymon11539990nonexistent-1.892.893-46.21.3545099.10
7609836blue-collarsinglebasic.9yunknownnonotelephonemaytue20129990nonexistent1.193.994-36.44.8575191.00
8625236blue-collarmarriedbasic.9ynoyesnotelephonemaytue9329990nonexistent1.193.994-36.44.8575191.00
91337328techniciansingleuniversity.degreenoyesnocellularjulwed17479990nonexistent1.493.918-42.74.9625228.10

Last rows

idagejobmaritaleducationdefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
329404109031admin.singleuniversity.degreenonoyescellularnovwed212236success-1.194.767-50.81.0484963.60
329411602333blue-collarmarriedbasic.6ynoyesnocellularjultue37619990nonexistent1.493.918-42.74.9615228.10
329422196243managementmarrieduniversity.degreenononocellularaugwed39829990nonexistent1.493.444-36.14.9645228.10
329433719458unemployeddivorcedprofessional.coursenonoyescellularaugthu14219990nonexistent-2.992.201-31.40.8835076.20
329441685036managementmarriedunknownunknownyesnocellularjulthu17119990nonexistent1.493.918-42.74.9625228.10
32945626558retiredmarriedprofessional.courseunknownnonotelephonemaytue42729990nonexistent1.193.994-36.44.8575191.00
329461128437managementmarrieduniversity.degreenononotelephonejunthu28819990nonexistent1.494.465-41.84.9615228.10
329473815835admin.marriedhigh.schoolnoyesnocellularoctthu194141success-3.492.431-26.90.7545017.51
3294886040managementmarrieduniversity.degreenoyesnotelephonemaywed29529990nonexistent1.193.994-36.44.8565191.00
329491579529admin.singleuniversity.degreenoyesnocellularjulmon33329990nonexistent1.493.918-42.74.9605228.10